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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2407.19475 |
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| _version_ | 1866909272190746624 |
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| author | Gkikas, Stefanos Chatzaki, Chariklia Tsiknakis, Manolis |
| author_facet | Gkikas, Stefanos Chatzaki, Chariklia Tsiknakis, Manolis |
| contents | Pain is a complex phenomenon which is manifested and expressed by patients in various forms. The immediate and objective recognition of it is a great of importance in order to attain a reliable and unbiased healthcare system. In this work, we elaborate electrocardiography signals revealing the existence of variations in pain perception among different demographic groups. We exploit this insight by introducing a novel multi-task neural network for automatic pain estimation utilizing the age and the gender information of each individual, and show its advantages compared to other approaches. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_19475 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Multi-task Neural Networks for Pain Intensity Estimation using Electrocardiogram and Demographic Factors Gkikas, Stefanos Chatzaki, Chariklia Tsiknakis, Manolis Artificial Intelligence Computer Vision and Pattern Recognition Pain is a complex phenomenon which is manifested and expressed by patients in various forms. The immediate and objective recognition of it is a great of importance in order to attain a reliable and unbiased healthcare system. In this work, we elaborate electrocardiography signals revealing the existence of variations in pain perception among different demographic groups. We exploit this insight by introducing a novel multi-task neural network for automatic pain estimation utilizing the age and the gender information of each individual, and show its advantages compared to other approaches. |
| title | Multi-task Neural Networks for Pain Intensity Estimation using Electrocardiogram and Demographic Factors |
| topic | Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2407.19475 |